Tracking Emotions: Intrinsic Motivation Grounded on Multi - Level Prediction Error Dynamics

We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics. This architecture modulates exploration noise and leverages computational resources according to the dynamics of the overall performance of the learning system. Results show that this architecture outperforms intrinsic motivation approaches where exploratory noise and goals are fixed. We suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error. We argue about the potential relationship between emotional valence and rates of progress toward a goal.